Conventional flow diagrams are useful in representing structure and flow in a complex system or network. Typical flow diagrams illustrate an order of interactions (e.g., data transfers) between components or participants in the system or network. In this way, typical flow charts are suitable for illustrating event flow participation of a single participant engaged in a sequence of events over a certain period of time. However, typical flow diagrams fail to convey information regarding quantitative distribution of values involved in the interactions between components. Thus, while typical flow diagrams are useful in illustrating an event flow of a single participant, typical flow diagrams are not suitable to illustrate event flows of a group of participants engaged in multiple concurrent events. For example, though a typical flow chart may be suitable to illustrate a sequence of television shows viewed by a single viewer over a certain time period, a single typical flow chart would be unable to suitably illustrate an aggregate sequence of television shows viewed by a group of viewers over the same time period because the group of viewers are likely to be watching multiple different television shows during the same blocks of time. Additionally, although information regarding the individual participation of individual participants may be maintained in sources such as tables of numerical data, deriving an aggregate event flow of all such users from such sources can be difficult due to challenges involved in analyzing and understanding a bulk set of raw numerical data.